Assessing structural health of helicopter fuselage panels through artificial neural networks hierarchies

Candelieri, Antonio; Sormani, Raul; Arosio, G; Giordani, Ilaria; Archetti, Francesco

doi:10.1504/ijrs.2013.057091

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…Since its introduction in the 1960s, ANNs continued to provide a powerful framework for modeling nonlinear systems, and they were used in a wide variety of engineering applications, including automatic control [ 24 ], solar energy systems [ 25 ], traffic and transportation [ 26 ], image processing [ 27 ], optimization of structures [ 28 ], materials science and engineering [ 29 , 30 , 31 , 32 ], manufacturing [ 33 ], fracture mechanics, and fault detection [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. In fracture mechanics, ANNs were mostly used in applications concerned with crack propagation, fatigue life, and failure mode prediction [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

“…The coupled proposed methodology provided useful means for achieving more adaptive aircraft control. Candelieri et al [ 51 ] used ANN for the diagnosis and prognosis assessment of the structural health of aircraft. Mainly, the diagnosis is related to crack detection in terms of size and location identification—that is, if cracking has occurred in the bay or stringer components.…”

Section: Introductionmentioning

confidence: 99%

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Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks

2020

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Multiple site damage (MSD) cracks are small fatigue cracks that may accumulate at the sides of highly loaded holes in aging aircraft structures. The presence of MSD cracks can drastically reduce the residual strength of fuselage panels. In this paper, artificial neural networks (ANN) modeling is used for predicting the residual strength of aluminum panels with MSD cracks. Experimental data that include 147 unique configurations of aluminum panels with MSD cracks are used. The experimental dataset includes three different aluminum alloys (2024-T3, 2524-T3, and 7075-T6), four different test panel configurations (unstiffened, stiffened, stiffened with a broken middle stiffener, and bolted lap-joints), many different panel widths and thicknesses, and the sizes of the lead and MSD cracks. The results presented in this paper demonstrate that a single ANN model can predict the residual strength for all materials and configurations with high accuracy. Specifically, the overall mean absolute error for the ANN model predictions is 3.82%. Furthermore, the ANN model residual strength predictions are compared to those obtained using the most accurate semi-analytical and computational approaches from the literature. The ANN model predictions are found to be at the same accuracy level of these approaches, and they even outperform the other approaches for many configurations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks

2020

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“…Therefore, different methods have been developed and implemented in ANN modeling to cope with the small dataset conditions. Examples of these methods include using simulated data [37], using virtual data [38], using multiple runs for models development and surrogate data analysis for model validation [39], using duplicated experimental runs [9], using stacked auto-encoder pre-training [14], using analytical models with errors revised by intelligent algorithms [35], using optimization aided generalized regression approach [36], and simultaneously considering data samples with their posterior probabilities [40]. Candelieri et al [37] used datasets obtained from finite element simulations to develop an ANN model for diagnosing and predicting cracks in aircraft fuselage panels.…”

Section: Introductionmentioning

confidence: 99%

“…Examples of these methods include using simulated data [37], using virtual data [38], using multiple runs for models development and surrogate data analysis for model validation [39], using duplicated experimental runs [9], using stacked auto-encoder pre-training [14], using analytical models with errors revised by intelligent algorithms [35], using optimization aided generalized regression approach [36], and simultaneously considering data samples with their posterior probabilities [40]. Candelieri et al [37] used datasets obtained from finite element simulations to develop an ANN model for diagnosing and predicting cracks in aircraft fuselage panels. The use of simulated data was shown to be technically effective and economical compared to generating an experimental dataset; however, the attainable accuracy of such approach is practically limited by the accuracy of the simulation method used to generate the dataset.…”

Section: Introductionmentioning

confidence: 99%

A Novel Assisted Artificial Neural Network Modeling Approach for Improved Accuracy Using Small Datasets: Application in Residual Strength Evaluation of Panels with Multiple Site Damage Cracks

2020

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An artificial neural network (ANN) extracts knowledge from a training dataset and uses this acquired knowledge to forecast outputs for any new set of inputs. When the input/output relations are complex and highly non-linear, the ANN needs a relatively large training dataset (hundreds of data points) to capture these relations adequately. This paper introduces a novel assisted-ANN modeling approach that enables the development of ANNs using small datasets, while maintaining high prediction accuracy. This approach uses parameters that are obtained using the known input/output relations (partial or full relations). These so called assistance parameters are included as ANN inputs in addition to the traditional direct independent inputs. The proposed assisted approach is applied for predicting the residual strength of panels with multiple site damage (MSD) cracks. Different assistance levels (four levels) and different training dataset sizes (from 75 down to 22 data points) are investigated, and the results are compared to the traditional approach. The results show that the assisted approach helps in achieving high predictions’ accuracy (<3% average error). The relative accuracy improvement is higher (up to 46%) for ANN learning algorithms that give lower prediction accuracy. Also, the relative accuracy improvement becomes more significant (up to 38%) for smaller dataset sizes.

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A Hyper-solution Framework for SVM Classification: Improving Damage Detection on Helicopter Fuselage Panels

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Assessing structural health of helicopter fuselage panels through artificial neural networks hierarchies

Cited by 10 publications

References 11 publications

Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks

Residual Strength Prediction of Aluminum Panels with Multiple Site Damage Using Artificial Neural Networks

A Novel Assisted Artificial Neural Network Modeling Approach for Improved Accuracy Using Small Datasets: Application in Residual Strength Evaluation of Panels with Multiple Site Damage Cracks

A Hyper-solution Framework for SVM Classification: Improving Damage Detection on Helicopter Fuselage Panels

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